No abstract

No abstract

ObjectiveTo provide a complete toxicity profile, toxicity spectrum, and a safety ranking of immune checkpoint inhibitor (ICI) drugs for treatment of cancer.DesignSystematic review and network meta-analysis.Data sourcesElectronic databases (PubMed, Embase, Cochrane Library, and Web of Science) were systematically searched to include relevant studies published in English between January 2007 and February 2018.Review methodsOnly head-to-head phase II and III randomised controlled trials comparing any two or three of the following treatments or different doses of the same ICI drug were included: nivolumab, pembrolizumab, ipilimumab, tremelimumab, atezolizumab, conventional therapy (chemotherapy, targeted therapy, and their combinations), two ICI drugs, or one ICI drug with conventional therapy. Eligible studies must have reported site, organ, or system level data on treatment related adverse events. High quality, single arm trials and placebo controlled trials on ICI drugs were selected to establish a validation group.Results36 head-to-head phase II and III randomised trials (n=15 370) were included. The general safety of ICI drugs ranked from high to low for all adverse events was as follows: atezolizumab (probability 76%, pooled incidence 66.4%), nivolumab (56%, 71.8%), pembrolizumab (55%, 75.1%), ipilimumab (55%, 86.8%), and tremelimumab (54%, not applicable). The general safety of ICI drugs ranked from high to low for severe or life threatening adverse events was as follows: atezolizumab (49%, 15.1%), nivolumab (46%, 14.1%), pembrolizumab (72%, 19.8%), ipilimumab (51%, 28.6%), and tremelimumab (28%, not applicable). Compared with conventional therapy, treatment-related adverse events for ICI drugs occurred mainly in the skin, endocrine, hepatic, and pulmonary systems. Taking one ICI drug was generally safer than taking two ICI drugs or one ICI drug with conventional therapy. Among the five ICI drugs, atezolizumab had the highest risk of hypothyroidism, nausea, and vomiting. The predominant treatment-related adverse events for pembrolizumab were arthralgia, pneumonitis, and hepatic toxicities. The main treatment-related adverse events for ipilimumab were skin, gastrointestinal, and renal toxicities. Nivolumab had a narrow and mild toxicity spectrum, mainly causing endocrine toxicities. Integrated evidence from the pooled incidences, subgroup, and sensitivity analyses implied that nivolumab is the best option in terms of safety, especially for the treatment of lung cancer.ConclusionsCompared with other ICI drugs used to treat cancer, atezolizumab had the best safety profile in general, and nivolumab had the best safety profile in lung cancer when taking an integrated approach. The safety ranking of treatments based on ICI drugs is modulated by specific treatment-related adverse events.Systematic review registrationPROSPERO CRD42017082553.

Background: Targeting the immune checkpoint pathway has demonstrated antitumor cytotoxicity in treatment-refractory head and neck squamous cell carcinoma (HNSC). To understand the molecular mechanisms underpinning its antitumor response, we characterized the immune landscape of HNSC by their tumor and stromal compartments to identify novel immune molecular subgroups. Patients and methods:A training cohort of 522 HNSC samples from the Cancer Genome Atlas profiled by RNA sequencing was analyzed. We separated gene expression patterns from tumor, stromal, and immune cell gene using a non-negative matrix factorization algorithm. We correlated the expression patterns with a set of immune-related gene signatures, potential immune biomarkers, and clinicopathological features. Six independent datasets containing 838 HNSC samples were used for validation.Results: Approximately 40% of HNSCs in the cohort (211/522) were identified to show enriched inflammatory response, enhanced cytolytic activity, and active interferon-c signaling (all, P < 0.001). We named this new molecular class of tumors the Immune Class. Then we found it contained two distinct microenvironment-based subtypes, characterized by markers of active or exhausted immune response. The Exhausted Immune Class was characterized by enrichment of activated stroma and antiinflammatory M2 macrophage signatures, WNT/transforming growth factor-b signaling pathway activation and poor survival (all, P < 0.05). An enriched proinflammatory M1 macrophage signature, enhanced cytolytic activity, abundant tumor-infiltrating lymphocytes, high human papillomavirus (HPV) infection, and favorable prognosis were associated with Active Immune Class (all, P < 0.05). The robustness of these immune molecular subgroups was verified in the validation cohorts, and Active Immune Class showed potential response to programmed cell death-1 blockade (P ¼ 0.01).

Purpose: We aimed to evaluate the value of deep learning on positron emission tomography with computed tomography (PET/CT)-based radiomics for individual induction chemotherapy (IC) in advanced nasopharyngeal carcinoma (NPC). Experimental Design: We constructed radiomics signatures and nomogram for predicting disease-free survival (DFS) based on the extracted features from PET and CT images in a training set (n ¼ 470), and then validated it on a test set (n ¼ 237). Harrell's concordance indices (C-index) and time-independent receiver operating characteristic (ROC) analysis were applied to evaluate the discriminatory ability of radiomics nomogram, and compare radiomics signatures with plasma Epstein-Barr virus (EBV) DNA. Results: A total of 18 features were selected to construct CT-based and PET-based signatures, which were significantly associated with DFS (P < 0.001). Using these signatures , we proposed a radiomics nomogram with a C-index of 0.754 [95% confidence interval (95% CI), 0.709-0.800] in the training set and 0.722 (95% CI, 0.652-0.792) in the test set. Consequently, 206 (29.1%) patients were stratified as high-risk group and the other 501 (70.9%) as low-risk group by the radiomics nomogram, and the corresponding 5-year DFS rates were 50.1% and 87.6%, respectively (P < 0.0001). High-risk patients could benefit from IC while the low-risk could not. Moreover, radiomics nomogram performed significantly better than the EBV DNA-based model (C-index: 0.754 vs. 0.675 in the training set and 0.722 vs. 0.671 in the test set) in risk stratification and guiding IC. Conclusions: Deep learning PET/CT-based radiomics could serve as a reliable and powerful tool for prognosis prediction and may act as a potential indicator for individual IC in advanced NPC.